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脑白质病变的空间分布可预测向轻度认知障碍和痴呆的进展。

Spatial distribution of cerebral white matter lesions predicts progression to mild cognitive impairment and dementia.

机构信息

Inserm, U1061, La Colombière Hospital, Montpellier, France.

出版信息

PLoS One. 2013;8(2):e56972. doi: 10.1371/journal.pone.0056972. Epub 2013 Feb 14.

DOI:10.1371/journal.pone.0056972
PMID:23457645
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3572965/
Abstract

CONTEXT

White matter lesions (WML) increase the risk of dementia. The relevance of WML location is less clear. We sought to determine whether a particular WML profile, based on the density and location of lesions, could be associated with an increased risk of mild cognitive impairment (MCI) or dementia over the following 7 years.

METHODS

In 426 healthy subjects from a cohort of community-dwelling people aged 65 years and over (ESPRIT Project), standardized cognitive and neurological evaluations were repeated after 2, 4 and 7 years. Patterns of WML were computed with a supervised data mining approach (decision trees) using the regional WML volumes (frontal, parietal, temporal, and occipital regions) and the total WML volume estimated at baseline. Cox proportional hazard models were then constructed to study the association between WML patterns and risk of MCI/dementia.

RESULTS

Total WML volume and percentage of WML in the temporal region proved to be the best predictors of progression to MCI and dementia. Specifically, severe total WML load with a high proportion of lesions in the temporal region was significantly associated with the risk of developing MCI or dementia.

CONCLUSIONS

Above a certain threshold of damage, a pattern of WML clustering in the temporal region identifies individuals at increased risk of MCI or dementia. As this WML pattern is observed before the onset of clinical symptoms, it may facilitate the detection of patients at risk of MCI/dementia.

摘要

背景

脑白质病变(WML)会增加痴呆的风险。WML 位置的相关性尚不清楚。我们旨在确定一种基于病变密度和位置的特定 WML 模式是否与随后 7 年内轻度认知障碍(MCI)或痴呆的风险增加相关。

方法

在年龄在 65 岁及以上的社区居住人群队列的 426 名健康受试者中(ESPRIT 项目),在 2、4 和 7 年后重复进行标准化认知和神经评估。使用基于监督数据挖掘方法(决策树)计算 WML 模式,使用基线时的区域性 WML 容积(额叶、顶叶、颞叶和枕叶区域)和总 WML 容积。然后构建 Cox 比例风险模型,以研究 WML 模式与 MCI/痴呆风险之间的关联。

结果

总 WML 容积和颞叶 WML 比例被证明是进展为 MCI 和痴呆的最佳预测因子。具体而言,颞叶区域存在严重的总 WML 负荷和高比例的病变与发生 MCI 或痴呆的风险显著相关。

结论

在超过一定损伤阈值的情况下,颞叶区域的 WML 聚类模式可识别出 MCI 或痴呆风险增加的个体。由于这种 WML 模式在出现临床症状之前就已经观察到,因此它可能有助于检测出有 MCI/痴呆风险的患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99f8/3572965/bc9a418f0337/pone.0056972.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99f8/3572965/13efbf50375c/pone.0056972.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99f8/3572965/fb9aa53d537f/pone.0056972.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99f8/3572965/bc9a418f0337/pone.0056972.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99f8/3572965/13efbf50375c/pone.0056972.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99f8/3572965/fb9aa53d537f/pone.0056972.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99f8/3572965/bc9a418f0337/pone.0056972.g003.jpg

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